This is the second post on the topic of technology, alienation and the role of education, with a particular focus on the consequences for teaching and learning. The first post was a general introduction to the topic. This post focuses on how technology can lead to alienation, and provides a framework for discussing the possibility of technology alienation in online learning and how to deal with it.

What do I mean by ‘alienation’?

Alienation is a term that has been around for some time. Karl Marx described alienation as the perception by people that they are becoming increasingly unable to control the social forces that shape their lives. Ultimately, highly alienated workers come to lose the sense that they can control any aspect of their lives, whether at work or at home, and become highly self-estranged. Such people are profoundly discontent, prone to alcohol and drug abuse, mental illness, violence, and the support of extreme social and political movements (Macionis and Plummer, 2012). Although Marx had an industrial society in mind, the definition works equally well to describe some of the negative effects of a digital society, as we shall see.

Causes

There are of course many different but related causes of alienation today:

the increasing inequality in wealth and in particular the perception by unemployed or low paid workers that they are being ‘passed by’ or not included in the wealth-generating economy. The feeling is particularly strong among workers who previously had well paid jobs (or expectations of well paid jobs) in manufacturing but have seen those jobs disappear in their lifetime. However, there are now also growing numbers of well educated younger people struggling to find well paid work while at the same time carrying a large debt as a result of increasingly expensive higher education;

one reason for the loss of manufacturing jobs is the effect of globalization: jobs going abroad to countries where the cost of labour is lower;

dysfunctional political systems are another factor, where people feel that they have little or no control over decisions made by government, that government is controlled by those with power and money, and political power is used to protect the ‘elites’;

lastly, and the main consideration in these posts, the role of technology, which operates in a number of ways that create alienation:

the most immediate is its role in replacing workers, originally in manufacturing, but now increasingly in service or even professional areas of work, including education;

a more subtle but nevertheless very powerful way in which technology leads to alienation is in controlling what we do, and in particular removing choice or decision-making from individuals. I will give some examples later;

lastly, many people are feeling increasingly exploited by technology companies collecting personal data and using it for commercial purposes or even to deny services such as insurance; in particular, the benefits to the end-user of technology seem very small compared to the large profits made by the companies that provide the services.

Symptoms

Here are some examples of how technology leads to alienation.

There have been several cases where intimate images of people have been posted on the Internet, without permission, and yet it has been impossible for the victims to get the images removed, at least until well after the damage has been done. The Erin Andrews case is the most recent, and the suicide of the 15 year old Amanda Todd is another example. These are extreme cases, but illustrate the perception that we have less and less control over social media and its potentially negative impact on their lives.

Sometimes the alienation comes from decisions made by engineers that pre-empt or deny human decision-making. I have always driven BMWs. Even when I had little money, I would buy a second hand BMW, mainly because of its superb engineering. However, I am driven crazy by my latest purchase. The ignition switches off automatically when I stop the car and automatically switches on again when I take my foot off the brake. One day I drove into my garage. I had stopped the car, and turned round to get something off the back seat. I took my foot off the brake and the car lurched forward and hit the freezer we have in the garage. If I had been on the street and done that, I could well have hit another car or even a pedestrian. The car also automatically locks the passenger doors. I have parked the car and started to walk away only to see my passengers pounding on the window to get out. I could cite nearly a hundred instances from this one car of decisions made by engineers that I don’t want made for me. In most cases (but not all) these default conditions can be changed, but that requires going through a 600 page printed manual. Furthermore these ‘features’ all cost money to install, money I would rather not pay if I had a choice.

We are just starting to see similar decisions by engineers creeping into online learning. One of the most popular uses of data analytics is to identify students ‘at risk’ of non-completion. As with the features in a car, there are potential benefits in this. However, the danger is that decisions based on correlations of other students’ previous behaviour with course completion may end up denying access to a program for a student considered ‘at risk’ but who may nevertheless might well succeed. In particular it could negatively profile black students in the USA, aboriginal students in Canada, or students from low income families.

A framework for discussion

I am dealing here with a highly emotive issue, and one where there will be many different and often contradictory perspectives. Let’s start with the ‘moral’ or ‘value’ issues. I start from the position that alienation is to be avoided if at all possible. It leads to destructive forces. In education in particular, alienation is the opposite of engagement, and for me, engagement is critical for student success. On the other hand, if people are really suffering, then alienation may well be a necessary starting point on the road to change or revolution. So it is difficult to adopt an objective stance to this topic. I want therefore to focus the discussion around the following issues:

what are the main developments in online learning that are occurring or will occur over the next few years?

who are the main drivers of change in this area?

what is the main value proposition? Why is this area being promoted? Who stands to benefit most from this development?

what are the risks or what is the downside of these developments? In particular, what is the risk that such developments may actually increase alienation in learners?

I will look at each of the following developments in the next series of blog posts within this framework, developments in online learning that have great promise but at the same time could, if not carefully managed, end up increasing alienation:

You are probably, like me, getting tired of the different predictions for 2016. So I’m not going to do my usual look forward for the year for individual developments in online learning. Instead, I want to raise a fundamental question about which direction online learning should be heading in the future, because the next year could turn out to be very significant in determining the future of online learning.

The key question we face is whether online learning should aim to replace teachers and instructors through automation, or whether technology should be used to empower not only teachers but also learners. Of course, the answer will always be a mix of both, but getting the balance right is critical.

An old but increasingly important question

This question, automation or human empowerment, is not new. It was raised by B.F. Skinner (1968) when he developed teaching machines in the early 1960s. He thought teaching machines would eventually replace teachers. On the other hand, Seymour Papert (1980) wanted computing to empower learners, not to teach them directly. In the early 1980s Papert got children to write computer code to improve the way they think and to solve problems. Papert was strongly influenced by Jean Piaget’s theory of cognitive development, and in particular that children constructed rather than absorbed knowledge.

In the 1980s, as personal computers became more common, computer-assisted learning (CAL or CAD) became popular, using computer-marked tests and early forms of adaptive learning. Also in the 1980s the first developments in artificial intelligence were applied, in the form of intelligent math tutoring. Great predictions were made then, as now, about the potential of AI to replace teachers.

Then along came the Internet. Following my first introduction to the Internet in a friend’s basement in Vancouver, I published an article in the first edition of the Journal of Distance Education, entitled ‘Computer-assisted learning or communications: which way for IT in distance education?’ (1986). In this paper I argued that the real value of the Internet and computing was to enable asynchronous interaction and communication between teacher and learners, and between learners themselves, rather than as teaching machines. This push towards a more constructivist approach to the use of computing in education was encapsulated in Mason and Kaye’s book, Mindweave (1989). Linda Harasim has since argued that online collaborative learning is an important theory of learning in its own right (Harasim, 2012).

In the 1990s, David Noble of York University attacked online learning in particular for turning universities into ‘Digital Diploma Mills’:

‘universities are not only undergoing a technological transformation. Beneath that change, and camouflaged by it, lies another: the commercialization of higher education.’

Noble (1998) argued that

‘high technology, at these universities, is often used not to ……improve teaching and research, but to replace the visions and voices of less-prestigious faculty with the second-hand and reified product of academic “superstars”.

However, contrary to Noble’s warnings, for fifteen years most university online courses followed more the route of interaction and communication between teachers and students than computer-assisted learning or video lectures, and Noble’s arguments were easily dismissed or forgotten.

Then along came lecture capture and with it, in 2011, Massive Open Online Courses (xMOOCs) from Coursera, Udacity and edX, driven by elite, highly selective universities, with their claims of making the best professors in the world available to everyone for free. Noble’s nightmare suddenly became very real. At the same time, these MOOCs have resulted in much more interest in big data, learning analytics, a revival of adaptive learning, and claims that artificial intelligence will revolutionize education, since automation is essential for managing such massive courses.

Thus we are now seeing a big swing back to the automation of learning, driven by powerful computing developments, Silicon Valley start-up thinking, and a sustained political push from those that want to commercialize education (more on this later). Underlying these developments is a fundamental conflict of philosophies and pedagogies, with automation being driven by an objectivist/behaviourist view of the world, compared with the constructivist approaches of online collaborative learning.

In other words, there are increasingly stark choices to be made about the future of online learning. Indeed, it is almost too late – I fear the forces of automation are winning – which is why 2016 will be such a pivotal year in this debate.

Automation and the commercialization of education

These developments in technology are being accompanied by a big push in the United States, China, India and other countries towards the commercialization of online learning. In other words, education is being seen increasingly as a commodity that can be bought and sold. This is not through the previous and largely discredited digital diploma mills of the for-profit online universities such as the University of Phoenix that David Noble feared, but rather through the encouragement and support of commercial computer companies moving into the education field, companies such as Coursera, Lynda.com and Udacity.

Audrey Watters and EdSurge both produced lists of EdTech ‘deals’ in 2015 totalling between $1-$2 billion. Yes, that’s right, that’s $1-$2 billion in investment in private ed tech companies in the USA (and China) in one year alone. At the same time, entrepreneurs are struggling to develop sustainable business models for ed tech investment, because with education funded publicly, a ‘true’ market is restricted. Politicians, entrepreneurs and policy makers on the right in the USA increasingly see a move to automation as a way of reducing government expenditure on education, and one means by which to ‘free up the market’.

Another development that threatens the public education model is the move by very rich entrepreneurs such as the Gates, the Hewletts and the Zuckerbergs to move their massive personal wealth into ‘charitable’ foundations or corporations and use this money for their pet ‘educational’ initiatives that also have indirect benefits for their businesses. Ian McGugan (2015) in the Globe and Mail newspaper estimates that the Chan Zuckerberg Initiative is worth potentially $45 billion, and one of its purposes is to promote the personalization of learning (another name hi-jacked by computer scientists; it’s a more human way of describing adaptive learning). Since one way Facebook makes its money is by selling personal data, forgive my suspicions that the Zuckerberg initiative is a not-so-obvious way of collecting data on future high earners. At the same time, the Chang Zuckerberg initiative enables the Zuckerberg’s to avoid paying tax on their profits from Facebook. Instead then of paying taxes that could be used to support public education, these immensely rich foundations enable a few entrepreneurs to set the agenda for how computing will be used in education.

Why not?

Technology is disrupting nearly every other business and profession, so why not education? Higher education in particular requires a huge amount of money, mostly raised through taxes and tuition fees, and it is difficult to tie results directly to investment. Surely we should be looking at ways in which technology can change higher education so that it is more accessible, more affordable and more effective in developing the knowledge and skills required in today’s and tomorrow’s society?

Absolutely. It is not so much the need for change that I am challenging, but the means by which this change is being promoted. In essence, a move to automated learning, while saving costs, will not improve the learning that matters, and particularly the outcomes needed in a digital age, namely, the high level intellectual skills of critical thinking, innovation, entrepreneurship, problem-solving , high-level multimedia communication, and above all, effective knowledge management.

To understand why automated approaches to learning are inappropriate to the needs of the 21st century we need to look particularly at the tools and methods being proposed.

The problems with automating learning

The main challenge for computer-directed learning such as information transmission and management through Internet-distributed video lectures, computer-marked assessments, adaptive learning, learning analytics, and artificial intelligence is that they are based on a model of learning that has limited applications. Behaviourism works well in assisting rote memory and basic levels of comprehension, but does not enable or facilitate deep learning, critical thinking and the other skills that are essential for learners in a digital age.

R. and D. Susskind (2015) in particular argue that there is a new age in artificial intelligence and adaptive learning driven primarily by what they call the brute force of more powerful computing. Why AI failed so dramatically in the 1980s, they argue, was because computer scientists tried to mimic the way that humans think, and computers then did not have the capacity to handle information in the way they do now. When however we use the power of today’s computing, it can solve previously intractable problems through analysis of massive amounts of data in ways that humans had not considered.

There are several problems with this argument. The first is that the Susskinds are correct in that computers operate differently from humans. Computers are mechanical and work basically on a binary operating system. Humans are biological and operate in a far more sophisticated way, capable of language creation as well as language interpretation, and use intuition as well as deductive thinking. Emotion as well as memory drives human behaviour, including learning. Furthermore humans are social animals, and depend heavily on social contact with other humans for learning. In essence humans learn differently from the way machine automation operates.

Unfortunately, computer scientists frequently ignore or are unaware of the research into human learning. In particular they are unaware that learning is largely developmental and constructed, and instead impose an old and less appropriate method of teaching based on behaviourism and an objectivist epistemology. If though we want to develop the skills and knowledge needed in a digital age, we need a more constructivist approach to learning.

Supporters of automation also make another mistake in over-estimating or misunderstanding how AI and learning analytics operate in education. These tools reflect a highly objectivist approach to teaching, where procedures can be analysed and systematised in advance. However, although we know a great deal about learning in general, we still know very little about how thinking and decision-making operate biologically in individual cases. At the same time, although brain research is promising to unlock some of these secrets, most brain scientists argue that while we are beginning to understand the relationship between brain activity and very specific forms of behaviour, there is a huge distance to travel before we can explain how these mechanisms affect learning in general or how an individual learns in particular. There are too many variables (such as emotion, memory, perception, communication, as well as neural activity) at play to find an isomorphic fit between the firing of neurons and computer ‘intelligence’.

The danger then with automation is that we drive humans to learn in ways that best suit how machines operate, and thus deny humans the potential of developing the higher levels of thinking that make humans different from machines. For instance, humans are better than machines at dealing with volatile, uncertain, complex and ambiguous situations, which is where we find ourselves in today’s society.

Lastly, both AI and adaptive learning depend on algorithms that predict or direct human behaviour. These algorithms though are not transparent to the end users. To give an example, learning analytics are being used to identify students at high risk of failure, based on correlations of previous behaviour online by previous students. However, for an individual, should a software program be making the decision as to whether that person is suitable for higher education or a particular course? If so, should that person know the grounds on which they are considered unsuitable and be able to challenge the algorithm or at least the principles on which that algorithm is based? Who makes the decision about these algorithms – a computer scientist using correlated data, or an educator concerned with equitable access? The more we try to automate learning, the greater the danger of unintended consequences, and the more need for educators rather than computer scientists to control the decision-making.

The way forward

In the past, I used to think of computer scientists as colleagues and friends in designing and delivering online learning. I am now increasingly seeing at least some of them as the enemy. This is largely to do with the hubris of Silicon Valley, which believes that computer scientists can solve any problem without knowing anything about the problem itself. MOOCs based on recorded lectures are a perfect example of this, being developed primarily by a few computer scientists from Stanford (and unfortunately blindly copied by many people in universities who should have known better.)

We need to start with the problem, which is how do we prepare learners for the knowledge and skills they will need in today’s society. I have argued (Bates, 2015) that we need to develop, in very large numbers of people, high level intellectual and practical skills that require the construction and development of knowledge, and that enable learners to find, analyse, evaluate and apply knowledge appropriately.

This requires a constructivist approach to learning which cannot be appropriately automated, as it depends on high quality interaction between knowledge experts and learners. There are many ways to accomplish this, and technology can play a leading role, by enabling easy access to knowledge, providing opportunities for practice in experientially-based learning environments, linking communities of scholars and learners together, providing open access to unlimited learning resources, and above all by enabling students to use technology to access, organise and demonstrate their knowledge appropriately.

These activities and approaches do not easily lend themselves to massive economies of scale through automation, although they do enable more effective outcomes and possibly some smaller economies of scale. Automation can be helpful in developing some of the foundations of learning, such as basic comprehension or language acquisition. But at the heart of developing the knowledge and skills needed in today’s society, the role of a human teacher, instructor or guide will remain absolutely essential. Certainly, the roles of teachers and instructors will need to change quite dramatically, teacher training and faculty development will be critical for success, and we need to use technology to enable students to take more responsibility for their own learning, but it is a dangerous illusion to believe that automation is the solution to learning in the 21st century.

Protecting the future

There are several practical steps that need to be taken to prevent the automation of teaching.

Educators – and in particular university presidents and senior civil servants with responsibility for education – need to speak out clearly about the dangers of automation, and the technology alternatives available that still exploit its potential and will lead to greater cost-effectiveness. This is not an argument against the use of technology in education, but the need to use it wisely so we get the kind of educated population we need in the 21st century.

Computer scientists need to show more respect to educators and be less arrogant. This means working collaboratively with educators, and treating them as equals.

We – teachers and educational technologists – need to apply in our own work and disseminate better to those outside education what we already know about effective learning and teaching.

Faculty and teachers need to develop compelling technology alternatives to automation that focus on the skills and knowledge needed in a digital age, such as:

networking learners online with working professionals, to solve real world problems (e.g. by developing a program similar to McMaster’s integrated science program for online/blended delivery)

building strong communities of practice through connectivist MOOCs (e.g. on climate change or mental health) to solve global problems

empowering students to use social media to research and demonstrate their knowledge through multimedia e-portfolios (e.g. UBC’s ETEC 522)

designing openly accessible high quality, student-activated simulations and games but designed and monitored by experts in the subject area.

Governments need to put as much money into research into learning and educational technology as they do into innovation in industry. Without better and more defensible theories of learning suitable for a digital age, we are open to any quack or opportunist who believes he or she has the best snake oil. More importantly, with better theory and knowledge of learning disseminated and applied appropriately, we can have a much more competitive workforce and a more just society.

We need to educate our politicians about the dangers of commercialization in education through the automation of learning and fight for a more equal society where the financial returns on technology applications are more equally shared.

Become edupunks and take back the web from powerful commercial interests by using open source, low cost, easy to use tools in education that protect our privacy and enable learners and teachers to control how they are used.

I find it a fun exercise to analyse the statistics for my blog at the end of the year, to see what were the most popular posts, as it gives some idea of the topics which have grabbed readers over the year. First let’s look at the figures for 2015 as a whole.

Overall visits

The total number of visits this year was way up from previous years. In 2013 the blog struggled to reach 20,000 visits a month most months. In 2014 the blog was averaging about 25,000 visits a month. In 2015 it was above 30,000 visits for nine of the twelve months and reached almost 40,000 visits in November, or an average of over 1,000 visits a day every day of the year.

It will be seen that the main reason for this large increase of visits in 2015 is indirectly related to the publication of Teaching in a Digital Age. I used my blog to ‘trial’ chapters and sections of the book, and it appears some of these blog posts are now being used as set readings on courses, or at least are being sought out by students studying, with students returning continually to these posts. I have no direct proof of this and if anyone using the blog can provide me with information on why they are using some of the blog posts on a regular basis, it will be appreciated.

Breakdown by sectors

Top posts in 2015

To understand the picture better we need to break this down into categories.

Category 1: Students seeking advice about online courses/programs

I have several posts that attract mainly students or potential students (rather than faculty or instructors), looking for advice on online or distance learning courses or programs. These would include:

1. The world’s largest supplier of free online learning?31,886 visits in 2015. No, interestingly, this isn’t about Coursera or Udacity, but Alison. Many students want to know about the status of the certificates as well as the courses offered by Alison, and the post has a regular and ongoing number of comments from people thinking about or who have taken Alison programs. The original post goes back to April, 2012 and has been one of the top posts every year since. However, the numbers this year almost tripled from 2014 (12,606).

3. Recommended graduate programs in e-learning. 20,180 visits in 2015. Now number 3, this has been the perennial number 1 until this year, despite the number of hits increasing from 16,715 in 2014. This was one of my first posts, going back to July 2008. Although I revise it regularly, it is probably a little out of date as there have been many new programs in this area developed in the last two or three years and I list only those I am familiar with.

7. Can you teach ‘real’ engineering at a distance?(posted 5 July, 2009). 6,388 visits in 2015. A great favourite of mine, especially since the answer has gradually changed over the six years since it was first posted (see my post on Trends in 2015), and again indicating the need for student guidance on choice of programs.

Although the blog is focused on faculty and instructors, it is clear from this data that there is high demand from students or potential students for independent, objective information about online courses and programs (also noted last year). The main value of the Alison post is the comments provided by actual users of the program.

I have hesitated to recommend online courses or programs in general, as I have no way of directly evaluating the vast majority, and there are so many of them now. However, it should be possible to create an independent, sponsor-free social media-based site where students can share information about different types of online courses and programs. I am sure such a web site would be very popular, but I’m leaving that to someone else to do (if it’s not already been done).

Category 2: Sections from ‘Teaching in a Digital Age.’

Graphic used for a short history of educational technology: Charlton Heston as Moses

The big difference this year is that many of my posts were ‘first drafts’ of sections or chapters of my book (many published though in 2014 as well as 2015). These have proved extremely popular with readers in 2015, accounting for nine of the top 27 posts for 2015:

2. A short history of educational technology (posted 10 December, 2014): 25,156 visits in 2015. It’s the second most popular post, and has always been among the top daily posts during 2015. This came as a complete surprise to me. I have no explanation why this is by far and away the most popular of the ‘book’ blog posts (three times more popular than the next book blog post). I’m assuming it’s a key reading for several courses, but why is it in constant demand almost every day? Am I being followed by a Charlton Heston fan club?

5. Learning theories and online learning (posted 14 July, 2014): 8,528 visits in 2015. This one is less surprising. This is a core or foundational topic for any program of study on online learning.

In the past, when I posted a new post, there was a flurry of visits in the first week or so, followed by a relatively small number of visits (less than 10 a day) in subsequent weeks. For the book posts though, the ‘tail’ is much thicker, with the above posts regularly getting between 30-50 visits each a day, and the order in terms of the number of visits doesn’t change a great deal from day to day.

Category 3: Single posts that won’t die

What’s right and what’s wrong with Coursera-style MOOCs: still popular

These are posts or topics that seem to have a life of their own. They are not tied in any direct way to the book.

4. What is distance education? (posted 7 July, 2008). 15,538 visits in 2015. This was one of my very first blog posts but it still gets many hits, probably driven by search engines in particular, and is obviously useful for students taking courses about online and distance education.

14. Ontario at last establishes “Ontario Online”(posted 13 January, 2014). 3,817 posts in 2015. I had to do a double take on the date of this one as I thought it was 2015. Obviously as this initiative swung into operation though, it attracted a good deal of attention, at least in Ontario, during 2015. It attracted little attention though in 2014, interestingly. Once again we are probably seeing the power of search engines, particularly if little has been published elsewhere.

19. What’s right and what’s wrong about Coursera-style MOOCs? (posted 5 August, 2012) 3,117 visits in 2015. Another popular blast from the past still getting some traction during 2015, but down from 4,170 visits in 2014. Nevertheless, probably used by potential MOOC students as well as instructors.

Category 4: New in 2015

You will have noticed that almost all the above posts were posted before 2015. So what was new in 2015? In all I posted 114 posts in 2015, down considerably from previous years. Roughly a third of these posts were ‘drafts’ from the book. Here’s what did make it into the top 25 posts:

15. Athabasca University’s troubles grow(posted 9 June 2015). 3,567 visits in 2015. This attracted a lot of attention, mainly from Athabasca University staff and students. The comments were particularly interesting.

In 2014, five posts published in 2014 made the top 25.

Lessons

What do I draw from this analysis?

first, this is a good example why data on its own isn’t very helpful. You need to know the reasons or the logic that drives the data. What do visits actually mean? One manic person visiting the same post thousands of times? Or thousands quickly glancing at something and finding no interest?

there seem though to be some drivers to individual posts:

search engines picking up on key words or phrases in the title of the post (e.g. competency-based learning)

blog posts that are now required or recommended readings for courses

students looking for some guides or hints about online courses and programs or about online learning in general

faculty or instructors looking for resources

topical or current news items.

the book, ‘Teaching in a Digital Age’, has both benefited from and driven people back to the blog. This is somewhat surprising, since the posts were often first drafts and the authoritative version is in the book, which is available from a totally different web site. Perhaps the blog though is more generally accessible or known. Again, comments from readers of the blog or the book on this issue would be welcome.

Blogging a lot less this year but doubling the number of visits does seem though to go against the first law of blogging, which is to blog every day if possible to drive traffic to your site. However, I suppose that once you have two thousand posts that have value to at least someone, the blog has its own life force. Nevertheless I will continue to add new posts during 2016.

What is clear to me is that the site now has developed a life of its own, as a set of resources on online learning that people keep coming back to. This is immensely rewarding, as this was always the intention.

In the meantime, have a great holiday and don’t spend too much time on screen!

This article presents a short written account and a video of the adaptive learning program being introduced at the University of Central Florida (which is one of the pioneers of blended learning). It is being used particularly in large classes to personalize the learning.

There’s not much detail in this article, and the program is in its early stages, but it is useful to know that UCF is experimenting with adaptive learning, given its long history of carefully evaluating its online learning initiatives.

I received this ‘gift’ from the Inholland Research Group Teaching and Learning with Technology. They took my ‘full’ CV (over 60 pages) and ran it through Wordle and produced this word cloud (appropriately in the shape of a plane, as I have a private pilot’s license). I haven’t updated my CV on my web site since 2008, so it doesn’t feature ‘online learning’ or ‘e-learning’ as much as it should, but otherwise it’s a fair reflection. Just a reminder though to keep my CV updated!

Perhaps more interesting is the second word cloud that they produced. They ran the entire text (all 500 pages) of Teaching in a Digital Age, and this is what Wordle produced:

I am very pleased that learning, teaching and students are the most used words in my book, with knowledge, skills, online, media and technology important but subsidiary. The word cloud really captures the issues and topics in the book and to some extent their relative importance. Also a few stylistic words popped up, such as ‘However’, ‘particular’, ‘different’ and ‘important’. Not sure what to make of that!

DISCLAIMER: The views and opinions expressed by Tony Bates, and all other participants in this blog, are the individual's views and opinions and do not necessarily reflect the views and opinions of Contact North | Contact Nord.